Optimized Smart Parking System Using Reinforcement Learning: Techniques for Efficient Urban Parking Management
  • Author(s): Uttam P. Kalsariya; Dr. Raghavendra R
  • Paper ID: 1717912
  • Page: 2006-2016
  • Published Date: 18-05-2026
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 9 Issue 11 May-2026
Abstract

Blistering urbanization and a growing number of cars have posed a serious problem of parking in ultramodern metropolises. It is a common occurrence that motorists waste a lot of time in the process of finding parking space which creates business traffic, destruction of energy and environmental pollution. Internet of effects( IoT) technologies have also been used to enable smart parking systems to cover parking spaces and provide motorists with real- time vacuity information. Being exploration has also made significant focus on detector grounded monitoring systems and parking central control platforms where parking data is collected and processed in real time. Other studies have combined machine literacy and IoT technologies to alleviate parking space discovery and operation systems. but majority of the systems are restricted in the aspect of scalability, high cost of structure, restricted ability to see content and in dynamic civic landscape, nondynamic decision- making.

Keywords

Smart Parking System, Reinforcement Learning, Internet of Things (IoT), Parking Slot Allocation, Intelligent Transportation Systems, Smart City Applications.

Citations

IRE Journals:
Uttam P. Kalsariya, Dr. Raghavendra R "Optimized Smart Parking System Using Reinforcement Learning: Techniques for Efficient Urban Parking Management" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2006-2016 https://doi.org/10.64388/IREV9I11-1717912

IEEE:
Uttam P. Kalsariya, Dr. Raghavendra R "Optimized Smart Parking System Using Reinforcement Learning: Techniques for Efficient Urban Parking Management" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717912